Multi-Vehicle Adaptive Planning with Online Estimated Cost Due to Disturbance Forces

  • Vishnu R. DesarajuEmail author
  • Lantao Liu
  • Nathan Michael
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


This paper proposes an adaptive planning architecture for multivehicle teams subject to an uncertain, spatially varying disturbance force. Motivated by a persistent surveillance task, the planning architecture is designed with three hierarchical levels. The highest level generates interference-free routes for the entire team to monitor areas of interest that have higher uncertainty. The lower level planners compute trajectories that can be tracked accurately along these routes by anticipating the effects of the disturbance force. To this end, the vehicles maintain an online estimate of the disturbance force, which drives adaptation at all planning levels. A set of simulation results validate the proposed method and demonstrate its utility for persistent surveillance.


Adaptive planning Vehicle routing Persistent surveillance 


  1. 1.
    Bethke, B., Bertuccelli, L.F., How, J.P.: Experimental demonstration of adaptive MDP-based planning with model uncertainty. In: Proc. of the AIAA Guidance, Navigation, and Control Conf., Honolulu, Hawaii (2008).Google Scholar
  2. 2.
    Garau, B., Alvarez, A., Oliver, G.: Path Planning of Autonomous Underwater Vehicles in Current Fields with Complex Spatial Variability: an A* Approach. In: Proc. of the IEEE Intl. Conf. on Robot. and Autom. (2005) 194–198.Google Scholar
  3. 3.
    Ceccarelli, N., Enright, J.J., Frazzoli, E., Rasmussen, S.J., Schumacher, C.J.: Micro UAV Path Planning for Reconnaissance in Wind. In: Proc. of the Amer. Control Conf. (July 2007) 5310–5315.Google Scholar
  4. 4.
    Desaraju, V.R., Michael, N.: Hierarchical adaptive planning in environments with uncertain, spatially-varying disturbance forces. In: Proc. of the IEEE Intl. Conf. on Robot. and Autom., Hong Kong, China (May 2014).Google Scholar
  5. 5.
    Jones, P.J.: Cooperative area surveillance strategies using multiple unmanned systems. In: PhD thesis, Georgia Institute of Technology. (2009).Google Scholar
  6. 6.
    Sundar, K., Rathinam, S.: Algorithms for routing an unmanned aerial vehicle in the presence of refueling depots. CoRR arXiv:1304.0494 (2013).
  7. 7.
    Bullo, F., Frazzoli, E., Pavone, M., Savla, K., Smith, S.: Dynamic vehicle routing for robotic systems. Proceedings of the IEEE 99(9) (2011) 1482–1504.CrossRefGoogle Scholar
  8. 8.
    Howard, A., Matarić, M.J., Sukhatme, G.S.: An incremental self-deployment algorithm for mobile sensor networks. Auton. Robots 13(2) (2002) 113–126.CrossRefzbMATHGoogle Scholar
  9. 9.
    Bellingham, J., Tillerson, M., Richards, A., How, J.P.: Multi-task allocation and path planning for cooperating UAVs. In: Cooperative Control: Models, Applications and Algorithms at the Conference on Coordination, Control and Optimization. (November 2001) 1–19.Google Scholar
  10. 10.
    Lagoudakis, M.G., Markakis, E., Kempe, D., Keskinocak, P., Kleywegt, A., Koenig, S., Tovey, C., Meyerson, A., Jain, S.: Auction-based multi-robot routing. In: Proc. of Robot.: Sci. and Syst. (2005) 343–350.Google Scholar
  11. 11.
    Kuhn, H.W.: The Hungarian Method for the Assignment Problem. Naval Research Logistic Quarterly 2 (1955) 83–97.CrossRefGoogle Scholar
  12. 12.
    Liu, L., Shell, D.A.: Physically routing robots in a multi-robot network: Flexibility through a three-dimensional matching graph. 32(12) (2013) 1475–1494.Google Scholar
  13. 13.
    Lee, T., Leok, M., McClamroch, N.H.: Geometric tracking control of a quadrotor UAV on SE(3). In: Proc. of the IEEE Conf. on Decision and Control, Atlanta, GA (December 2010) 5420–5425.Google Scholar
  14. 14.
    Shen, S., Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained MAV. In: Proc. of the IEEE Intl. Conf. on Robot. and Autom., Shanghai, China (May 2011).Google Scholar
  15. 15.
    Powers, C., Mellinger, D., Kushleyev, A., Kothmann, B., Kumar, V.: Influence of aerodynamics and proximity effects in quadrotor flight. In: Proc. of the Intl. Sym. on Exp. Robot., Quebec City, Canada (June 2012) 289–302.Google Scholar
  16. 16.
    Lawler, E.: Combinatorial Optimization: Networks and Matroids. Dover Publications, Mineola, NY (2001).Google Scholar
  17. 17.
    Kuwata, Y., Teo, J., Fiore, G., Karaman, S., Frazzoli, E., How, J.: Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 17(5) (2009) 1105–1118.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Vishnu R. Desaraju
    • 1
    Email author
  • Lantao Liu
    • 1
  • Nathan Michael
    • 1
  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations